Artificial intelligence system for genetic analysis

ABSTRACT

The present invention provides a complete artificial intelligence system for the acquisition and analysis of nucleic acid array hybridization information. The system includes a central data processing facility and one or more user facilities, linked by encrypted connections. Each user facility may include an optical scanning system to collect hybridization signals from a nucleic acid array, an image processing system to convert the optical data into a set of hybridization parameters, a connection to a data network, and a user interface to display, manipulate, search, and analyze hybridization information. This system reads data from a nucleic acid microarray, analyzes test results, evaluates patient risk for various ailments, and recommends methods of treatment. The automated artificial intelligence system is a real time, dynamic decision making tool that can be used in conjunction with a clinical analysis system, and with the information obtained in a research and development environment.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority under 35U.S.C. §120 to, U.S. application Ser. No. 09/650,005 filed Aug. 28,2000, which claims the benefit under 35 U.S.C. §119(e) of U.S.Provisional Application Ser. No. 60/151,258, filed Aug. 27, 1999, theentire contents of which applications are hereby incorporated byreference in their entirety.

FIELD OF THE INVENTION

The present invention relates to electronic genetic analysis systems,and more particularly, to a computerized artificial intelligence systemfor acquiring and processing DNA hybridization patterns and comparingthe processed patterns with databases for clinical or researchapplications.

BACKGROUND OF THE INVENTION

Nucleic acid analysis can provide important diagnostic and prognosticinformation in both clinical and research environments. Withamplification techniques such as the polymerase chain reaction, routineclinical samples can provide material for extensive genetic analysis ofknown traits. For example, the drug resistance characteristics of apathogen can be determined by genomic analysis if the sequence of genesor mutations conferring drug resistance is known. Knowledge of drugresistances allows design of an appropriate therapy. Similarly,screening for known mutations in cellular oncogenes can diagnose ordirect the treatment of cancer.

The advent of high-density nucleic acid hybridization devices, generallyknown as “DNA chips” or “nucleic acid arrays”, has greatly extended therange of possible clinical applications for nucleic acid analysis. Theability to perform simultaneously millions of nucleic acid hybridizationexperiments makes feasible large-scale screening assays on a singleclinical sample. For example, in U.S. Pat. No. 5,861,242, Chee et al.disclose DNA arrays designed to determine the complete nucleotidesequence of a segment of the HIV genome where mutations in the viralreverse transcriptase gene correlate with drug resistant phenotypes.Combined with appropriate amplification techniques, nucleic acid arraysbearing a battery of probes complementary to pathogen genomes can beused to rapidly screen clinical or industrial samples for hundreds orthousands of mutations, pathogens or contaminants.

Another application of high density nucleic acid arrays is in profilingthe genomic expression pattern of an organism. By measuring the degreeof hybridization of an RNA sample to an array of nucleic acid probes,each corresponding to a transcribed segment of the genome, it ispossible to simultaneously assess the expression level of many or all ofthe genes of an organism. In U.S. Pat. No. 6,040,138, Lockhart et al.describe methods of monitoring the expression levels of a multiplicityof genes, wherein a high density array contains oligonucleotide probescomplementary to target nucleic acids, including RNA transcripts. Thearrays are used to detect the presence or absence of target nucleic acidsequences, and to quantify the relative abundance of the targetsequences in a complex nucleic acid pool. Small variations in expressionlevels of a particular gene can be identified and quantified in acomplex population of genes that outnumber the target nucleic acids by amillion fold or more. In U.S. Pat. No. 6,004,755, Wang et al. describequantitative microarray hybridization assays, wherein end-labeled targetnucleic acids are contacted with an array of probe molecules stablyassociated with the surface of a solid support under hybridizationconditions. The resulting hybridization pattern can be used to obtainquantitative information about the genetic profile of the end-labeledtarget nucleic acid sample and the source from which it is derived.

Computer systems and electronic databases for the analysis of biologicalinformation are known in the art. Several types of electronic databasesare currently available, including genomic databases, medical diagnosticanalysis systems, and clinical information systems. U.S. Pat. No.5,966,712 encompasses a relational database system for storing andmanipulating biomolecular sequence information, including genomiclibraries for different types of organisms. Comparative Genomics is afeature of this database system which allows a user to compare thesequence data sets of different organism types. U.S. Pat. No. 6,063,026describes a computerized medical diagnostic method, including a databasecontaining diseases and indicators associated with each disease, and asecond database containing human test results associated with eachindicator. An individual's test results are compared with the seconddatabase to determine presence levels of each indicator to ultimatelyprovide a medical analysis of the individual and identify therapeutictreatments and drugs. The method is based on pattern matching ofdiseases associated with the various indicator presence levels. PCTpublication WO 99/04043 discloses Telemedicine, a computer system thatprovides for automatic test tracking and analysis. Test results andpatient profile medical history can be inputted into the system ornetwork and compared with databases of diseases, disorders, treatments,care plans, nutritional supplements, and medicine. This system cantransmit an analysis and proposed treatment to the patient's physicianor health care provider for approval before it is sent to the patient.This system is also used for automatic test tracking and reporting topublic health organizations.

Advances in the genomics and bioinformatics area, especially thedevelopment of gene chips and micro arrays, require more and moresophisticated bioinformatics tools for the manipulation and analysis ofgene expression data. Thus, attempts have been made to provide systemsthat simplify the analysis of micro array expression data. For example,PCT publication WO 00/28091 describes a system and integrated computersoftware programs for the manipulation and analysis of gene expressiondata, particularly suited for expression data generated with micro arraytechnologies. This system includes graphical tools, search and sortfunctions for viewing gene expression data, as well as a graphical userinterface for data clustering, browsing, and viewing. U.S. Pat. No.5,733,729 discloses a computer system for analyzing nucleic acidsequences, wherein the system is used to calculate probabilities fordetermining unknown bases by analyzing the fluorescence intensities ofhybridized nucleic acid probes on biological chips. This system usesinformation from multiple experiments to improve the accuracy of callingunknown bases.

As of today, no electronic system has yet been devised wherein nucleicacid expression patterns derived from high density arrays can beanalyzed, stored, manipulated, and compared; and then linked to patientprofiles, medical conditions, and treatments of various ailments anddiseases. Such a system would combine experimental hybridization dataanalysis and clinical applications. If a database of gene expressionpatterns reflecting distinct pathological or physiological states of thesampled tissue exists, comparison of the sample's gene expressionprofile with stored gene expression profiles can provide importantinformation about the biological state of the tissue. Such informationcould be used to assess a variety of biological states of interest, suchas neoplasia, cancer, immune response, environmental stress ornutritional condition, and the like. Such information could further beused to provide appropriate treatment for a variety of pathologicalconditions, ailments, and diseases.

The object of the present invention is to provide a system where nucleicacid array hybridization information is compared with a centralrepository of hybridization profiles to provide medical, experimental,or industrial analysis of biological samples. Another object of thepresent invention is to provide a system where analyzed nucleic acidarray hybridization information can be linked and correlated to patientprofiles, medical conditions, and treatments of various ailments anddiseases. Publications, patents, and other reference materials referredto herein are incorporated herein by reference.

SUMMARY OF THE INVENTION

The present invention provides a complete system for the acquisition andanalysis of nucleic acid array hybridization information in combinationwith a clinical analysis system and databank. This automated artificialintelligence system encompasses monitoring, screening, diagnosing, andperforming prognosis of disease(s) and condition(s) by integratingprimary and secondary genomic information, patient profiles, animals andcrops information, insects and other living organism profiles, anddisease models, using proprietary neural network algorithms. Theresulting information can be used for patient treatment analysis as wellas for research and development, particularly drug discovery. Inaddition, the system links internal and external clinical and researchdata bases; processes information in real-time; uses the Internet andother wireless technologies to transmit or receive information; providesaccess to information that is useful in managing disease outbreaks andemergency situations; provides tiered information access to doctors,patients, researchers, and others; performs simultaneous,multi-dimensional analysis; and analyzes genetic information byethnicity, region, occupation, age, sex, and the like. The automatedartificial intelligence system is a real time, dynamic decision makingtool that can be used not only in conjunction with a clinical analysissystem, but also with the information obtained in a research anddevelopment environment. Access to this system allows the user(s) tolook at both clinical and non-clinical information. Most importantly,the system is intelligent and possesses the capability to interpret theinformation obtained.

The system is divided into at least one central data processing facilityand one or more user facilities, linked by encrypted network connectionsor similar links. Each user facility may include an optical scanningsystem to collect hybridization signals from a nucleic acid array, animage processing system to convert the optical data into a set ofhybridization parameters, a connection to a data network, and a userinterface to display, manipulate, search, and analyze hybridizationinformation. Alternatively, the optical scanning system may collectsignals from a proteomics array or chip, and the image processing systemmay convert the optical data into a set of proteomics parameters. Theuser interface may be used to display, manipulate, search, and analyzeproteomics related information.

One aspect of the present invention provides at least one central dataprocessing facility, including a Web server or other mechanism (e.g.,Electronic Data Interchange (EDI), Dial-Up, etc.) that communicates withremote user facilities, receiving and transmitting hybridizationinformation, and supports data analyses, as well as providing securityand business functions. The central data processing facility furtherincludes a database server that stores hybridization profiles, patientprofiles, reference information, clinical information associated withhybridization profiles, statistical summaries, and the like. Mediatingbetween the Web server and the database server is an application server,which constructs queries for the database server and performsstatistical comparisons between hybridization parameters received by theWeb server and hybridization parameters supplied by the database server.

In one manner of practicing the invention, clinicians and otherlaboratory personnel, utilizing a nucleic acid array, collecthybridization information from a clinical sample and transmit thisinformation to a central data processing facility along with theidentity of the array. At the central data processing facility, thehybridization profile is compared with stored hybridization parameters,and artificial intelligence routines determine the most likelypathological or physiological conditions suggested by the hybridizationinformation. These possibilities, along with suggested methods oftreatment for the conditions, are returned to the user. The suggestedmethods of treatment may be chosen simply by reference to the indicatedpathological or physiological condition, or may be chosen for likelytherapeutic effectiveness based on particular hybridization parameters.In an alternative manner of practicing the invention, a proteomics chipmay be used instead of a nucleic acid array.

In some manners of practicing the invention, hybridization profilescollected by remote and/or local facilities include clinicalobservations or other information associated with each profile, and theprofile with its associated observations is added to the centraldatabase. In other manners of practicing the invention, hybridizationprofiles submitted to the central facility do not contain associatedobservations and are not added to the central database.

In another manner of practicing the invention, users perform statisticaltests on cataloged hybridization profiles stored in the central dataprocessing facility. By correlating the hybridization signal of one ormore probes in the array with clinical information recorded for eachhybridization profile, users create and test hypotheses relatinghybridization information to particular pathological or physiologicalstates. A variety of statistical analyses are provided to suggest andevaluate hypotheses.

BRIEF DESCRIPTION OF THE FIGURES

The present invention is best understood when read in conjunction withthe accompanying figures that serve to illustrate the preferredembodiments. It is understood, however, that the invention is notlimited to the specific embodiments disclosed in the figures.

FIG. 1 provides a flow chart of the artificial intelligence system andits architecture. The system is divided into at least one central dataprocessing facility and one or more user facilities, linked by encryptednetwork connections or similar links. The central data processingfacility includes a Web Server or other mechanism (e.g., Electronic DataInterchange (EDI), Dial-Up, etc.), Application Server, Database Server,and Operations Server. For the purpose of illustrating the instantinvention, a remote user facility is depicted, including a DiagnosticUser entity and a Browse User entity.

FIG. 1 depicts the flow of information between the server nodes, such asthe Web Server, Application Server, Database Server, and OperationsServer, wherein the information flows back and forth within the centraldata processing facility as shown by black connecting lines. FIG. 1further depicts the flow of information between a remote user facilityand the central data processing facility, wherein information flows backand forth between the Diagnostic User entity and the Web Server, as wellas the Browse User entity and the Web Server.

FIG. 1 also depicts the various functionalities within each server nodein the central data processing facility. The Web server includes, but isnot limited to, security functionality, products and companydescription, statistical summary of patient database, request toapplication server, and product ordering. The Application Serverincludes, but is not limited to, database (DB) query for chip identifier(ID), DB query for statistical data summary, pattern match statisticalprocessing, and sending results to DB and back to user functionality.The Database Server includes, but is not limited to, genetic pattern DBfor all chip ID, patient generic pattern DB, and statistical datasummary. The Operations Server includes, but is not limited to, ordermanagement, billing management, and order tracking.

FIG. 1 also depicts the various functionalities within each entity inthe user facility. The Diagnostic User entity includes, but is notlimited to, a DNA microarray or gene chip, an array or chip scanner, aPC system, a user interface for system operations, a generic patternprocessing functionality, request for pattern match for chip ID tocentral processing facility, and a report generation functionality.

FIG. 2 shows a possible schematic representation of the system design ofthe automated artificial intelligence system, including a Web ServerTier, Application Tier, and Database Tier.

FIG. 3 shows a possible schematic representation of the system scaling,including a Web Server Tier, Application Tier, and Database Tier.

DETAILED DESCRIPTION OF THE INVENTION a) DEFINITIONS AND GENERALPARAMETERS

The following definitions are set forth to illustrate and define themeaning and scope of the various terms used to describe the inventionherein.

A “polynucleotide”, “oligonucleotide”, or “nucleic acid” includes, butis not limited to, mRNA, cDNA, genomic DNA, and synthetic DNA and RNAsequences, comprising the natural nucleotide bases adenine, guanine,cytosine, thymine, and uracil. The terms also encompass sequences havingone or more modified nucleotide(s). The terms “polynucleotide” and“oligonucleotide” are used interchangeably herein. No limitation as tolength or to synthetic origin are suggested by the use of either ofthese terms herein.

A “probe” is a nucleic acid sequence, optionally tethered, affixed, orbound to a solid surface such as a microarray or chip.

A “target nucleic acid” is generally a free nucleic acid sample whoseidentity or/and abundance can be detected through the use of a DNA microarray.

The term “sequences which hybridize thereto” means polynucleotidesequences which are capable of forming Watson-Crick hydrogen bonds withanother polynucleotide sequence or probe that is bound to an array orchip. Although the sequences which hybridize to a polynucleotide orprobe may be about 90%-100% complementary to the polynucleotide orprobe, if the sequences are of sufficient length, in solutions with highsalt concentrations, and/or under low temperature conditions,polynucleotides with complementarity of 70% or above, or even just 50%or above, may hybridize to the polynucleotide or probe.

The terms “gene chip”, “DNA microarray”, “nucleic acid array”, and “genearray” are used interchangeably herein. These terms refer to a solidsubstrate, generally made of glass but sometimes made of nylon or othermaterials, to which probes with known identity are bound. The probes canhybridize to target nucleic acids through complementary binding, thusallowing parallel gene expression and gene discovery studies. Variantsof DNA microarray technology are known in the art. For example, cDNAprobes of about 500 to about 5,000 bases long can be immobilized to asolid surface such as glass using robot spotting and exposed to a set oftargets either separately or in a mixture. Alternatively, an array ofoligonucleotides of about 20mer to about 25mer or longer oligos orpeptide nucleic acid (PNA) probes is synthesized either in situ(on-chip) or by conventional synthesis followed by on-chipimmobilization. The array is exposed to labeled sample DNA, hybridized,and the identity and/or abundance of complementary sequences isdetermined.

The term “proteomics” is most broadly defined as the systematic analysisand documentation of proteins in biological samples. Proteomics is amass-screening approach to molecular biology, which aims to document theoverall distribution of proteins in cells, identify and characterizeindividual proteins of interest, and ultimately, elucidate theirrelationships and functional roles. The term “proteomics chip” orproteomics array” refers to a solid substrate to which proteins withknown identity are bound.

A “clinical sample” or “biological sample” may be a sample of tissue ora sample of body fluid. The term “tissue” is used herein to refer to anybiological matter made up of one cell, multiple cells, an agglomerationof cells, or an entire organ. The term tissue, as used herein,encompasses a cell or cells which can be either normal or abnormal (i.e.a tumor). A “body fluid” may be any liquid substance extracted,excreted, or secreted from an organism or a tissue of an organism. Thebody fluid need not necessarily contain cells. Body fluids of relevanceto the present invention include, but are not limited to, whole blood,serum, plasma, urine, cerebral spinal fluid, tears, and amniotic fluid.

b) THE ARTIFICIAL INTELLIGENCE SYSTEM

The present invention provides a complete artificial intelligence systemfor the acquisition and analysis of high-density and low-density nucleicacid array hybridization information. This system reads data from a genechip or DNA microarray, analyzes test results based on maintainedparameters, evaluates patient risk for various ailments, recommendsmethods of treatment, presents information to medical and/or privateindividuals, and notifies test participants when new treatment becomesavailable. The system captures data from a gene chip and stores testresults in a database using an optical scanning methodology. Gene chipsare controlled by using a unique inventory identifier (ID). Correlateddata may be collected by the medical practitioner or researcher andentered into the system via an electronic interface, in order to provideadditional information that may be used in various analyses. The testresults may be used to perform individual diagnostics, longitudinalstudies, population studies, or a wide variety of statistical analysesof patient data. The system also has embedded and/or linked software forplanning, manufacturing, quality assurance, processing, and tracking itsmicroarray products. Furthermore, the system presents the informationprimarily via a secured encrypted Web interface, such as the Internet.The information is also presented in a retrievable format, such aselectronic or paper format, using various computing technologies.

The artificial intelligence system allows for clinical, research anddevelopment related genetic testing. This system involves the use ofmicroarray technology in the form of a DNA chip in conjunction with afluidics station, chemical reagents, chemical fluorescence and anoptical reader or scanner system. Genetic testing is performed by usingchemical reagents to extract the DNA or RNA from a biological sample.Subsequently the prepared sample is applied to the DNA chip via thefluidics station. A DNA chip has a large number of spots, each of whichcorresponds to a specific nucleic acid sequence (e.g., nucleic acidprobe, genomic DNA, cDNA, etc.). The extent to which the hybridized DNAor RNA attaches to each spot on the chip indicates the level at which aspecific gene is expressed in the sample. Using the optical reader orscanner to image the hybridized DNA microarray can provide the means toquantify the gene expression levels for each spot. Image processingsoftware, hosted in the PC attached to the optical reader or scanner,operates on the raw image data to generate an optical intensitymeasurement for each spot for each fluorescent color used in the test.This indicates the “brightness or light intensity” of the spot for eachfluorescent color and the expression level for the gene sequencecorresponding to that spot. The artificial intelligence system or alinked software program converts the gene expression data to testresults data directly applicable to the clinical or research anddevelopment associated user. These test results include diagnosedmedical and/or clinical conditions for clinical users, and the systemalso provides an associated set of treatment options for the diagnosedclinical conditions.

The artificial intelligence system is divided into at least one centraldata processing facility and one or more remote and/or local userfacilities, linked by encrypted network connections or similar links.The architecture of this system is based on a shared processingfunctionality between remote or local user facilities including, but notlimited to, hospitals, clinics, research facilities, businesses, andnon-profit organizations; and a central location, such as a companycentralized location. The remote or local user facilities also include aWeb user or Internet user who requests information or orders products.FIG. 1 displays the system architecture of the instant invention. FIGS.2 and 3 depict the system design and scaling, respectively, relating tothe application tier and database tier.

In a preferred embodiment, each remote or local user facility includesan optical scanning system to collect hybridization signals from anucleic acid array, an image processing system to convert the opticaldata into a set of hybridization parameters, a connection to theInternet or other data network, and a user interface to display,manipulate, search, and analyze hybridization information. A potentiallylarge number of optical reader or scanner and PC systems may be deployedat user sites throughout the world. In an alternative embodiment, eachremote or local user facility includes a connection to the Internet orother data network, and a user interface to manipulate, search, analyze,and display data (e.g., hybridization information, patient information,statistical information, clinical and medical information, diagnosis andtreatment information, biological information, product information,company information, etc). The artificial intelligence system providesInternet access to diagnosis processing and associated treatmentinformation. The remote or local user facility comprises a diagnosticuser (e.g., hospital, clinic, research facility, business, non-profitorganization, and the like) and a browse user (e.g., Internet user). Thediagnostic user utilizes the system, including the fluidic station touse gene chips or DNA microarrays, the scanner and/or detector to readthe chip data, the memory storage to store the scanned chip data, and aPC or other desktop system to search, display, correlate, manipulate,and analyze data via a user interface. The memory storage can be locateddirectly in the scanner system. But the chip data may also be stored inthe PC associated with the scanner, or in both, the scanner system anddesktop system.

In an alternative embodiment, the optical scanning system may collectsignals from a proteomics array or chip, and the image processing systemmay convert the optical data into a set of proteomics parameters. Theuser interface may be used to display, manipulate, search, and analyzeproteomics related information. More specifically, information (e.g.,signals) may be collected from a proteomics chip, transmitted to thecentral data processing facility, analyzed to generate a proteomicsprofile, and compared to stored proteomics parameters to provideanalyzed data. The analyzed data can then be used to determinephysiological condition through the use of artificial intelligence.Methods of treatment based on the physiological condition(s) may berecommended.

Gene expression analysis and other specific, less comprehensivehybridization profile analysis can be performed in the remote or localuser facility which allows the system to be a stand alone system and itsimplifies the interface to the central system. Alternatively, a chipmay be scanned in the user facility resulting in raw scanned orpreprocessed data which can then be sent to the central processingfacility for further analysis. For example, a CD with raw data maybesent to the central processing facility where the data is analyzed.After further analysis, a genetic pattern emerges which can be comparedand correlated to existing data and matched to the application in thecentral processing facility.

There are two categories of diagnostic users, such as “diagnostic masterusers” and “diagnostic users”. Accounts for diagnostic master users areauthorized and correspond to the user sites where the systems aredeployed. These diagnostic master users are allowed to authorizeaccounts for diagnostic users. For clinical applications, diagnosticusers correspond to the individuals that have been tested. For researchand development applications, diagnostic master users can designateeither individual chip test results or groups of chips as a singlediagnostic user, wherein this option lies with the diagnostic masterusers in order to meet their testing and analysis needs.

Diagnosis processing is a key part of the artificial intelligencesystem. The diagnosis processing for clinical applications may bedifferent from that of research and development applications. Diagnosisprocessing for clinical applications implements a rules based analysisapplication which utilizes a database set of rules and results.Diagnosis processing thereby determines which conditions apply to thevarious combinations of gene expression levels and personal medicalhistory. For example, a cardiovascular chip for clinical applicationsmay include a wide variety of spots with identified genomic mutationsassociated with various cardiovascular conditions. The diagnosisprocessing for this chip is based on the expression levels for each ofthese gene sequences, using predefined rules to determine the likelihoodof a set of identified diseases or cancers. For example, the rules mightbe implemented as: If the gene sequences on spots 18, 52 and 115 havehigh expression levels, and if the gene sequences on spots 34, 88 and125 have low expression levels, and the individual has a family historyof heart disease, then this individual has a high likelihood ofdeveloping a specified heart disease within 5 years. Additionally, theremay be a database set of treatments developed for each diagnosedcondition. As each chip may utilize at least several thousand spots, thedatabase set of rules is complex. This type of processing is well suitedto employ expert systems and/or rules based processing applicationswhich are provided in the instant invention. The development of thesedatabase sets of rules and results include both, public information andprivate information. The databases of the instant invention continuallymature and develop into more and more complex systems as informationfrom public and private sources continues to be added to the existingdatabases.

Another aspect of the present invention provides at least one centraldata processing facility with dedicated servers for specific functions.The central data processing facility includes a Web server or othermechanism (e.g., Electronic Data Interchange (EDI), Dial-Up, etc.) thatcommunicates with remote user facilities, receiving and transmittinghybridization information, and supports data analyses, as well asproviding security and business functions. In particular, the Web servercomprises functions including, but not limited to product information,product ordering, company information, statistical summary of patientdatabase, request to the application server, and security. An overviewof the artificial intelligence system is shown in FIGS. 1, 2 and 3. Thecentral data processing facility further includes a database server thatstores hybridization profiles, patient profiles, reference information,clinical information associated with hybridization profiles, variousstatistical summaries, and the like. More specifically, the databaseserver comprises functions including, but not limited to genetic patterndatabase for chip ID, patient generic pattern database, and statisticaldata summary. Mediating between the Web server and the database serveris an application server, which constructs queries for the databaseserver and performs statistical comparisons between hybridizationparameters received by the Web server and hybridization parameterssupplied by the database server. In particular, the application servercomprises functions including, but not limited to database query forchip ID genetic pattern, database query for statistical data summary,pattern match statistical processing, and results output. The centralprocessing facility also includes an operations server wherein theoperations server comprises functions such as order management, billingmanagement, order tracking, and the like.

Another aspect of the present invention provides for a method, whereinclinicians and other laboratory personnel, utilizing a nucleic acidarray, collect hybridization information from a clinical sample andtransmit this information to a central data processing facility alongwith the identity of the array. In a preferred embodiment, thehybridization profile is compared with stored hybridization parametersat the central data processing facility, and artificial intelligenceroutines determine the most likely pathological or physiologicalconditions suggested by the hybridization information. Thesepossibilities, along with suggested methods of treatment for theconditions, are returned to the user. In an alternative embodiment, thehybridization profile is compared with stored hybridization parametersat the user facility and raw scanned or preprocessed data is then sentto the central processing facility for further analysis. For example, aCD with raw data maybe sent to the central processing facility where thedata is analyzed via artificial intelligence. Results are returned tothe user with suggested methods of treatment. The suggested methods oftreatment may be chosen simply by reference to the indicatedpathological or physiological condition, or may be chosen for likelytherapeutic effectiveness based on particular hybridization parameters.In an alternative method of the instant invention, a proteomics array orchip may be used instead of a nucleic acid array.

In a manners of practicing the invention, hybridization profilescollected by remote and/or local facilities include clinicalobservations or other information associated with each profile, and theprofile with its associated observations is added to the centraldatabase. In another manner of practicing the invention, hybridizationprofiles submitted to the central facility do not contain associatedobservations and are not added to the central database.

In yet another manner of practicing the invention, users performstatistical tests on cataloged hybridization profiles stored in thecentral data processing facility. By correlating the hybridizationsignal of one or more probes in the array with clinical informationrecorded for each hybridization profile, users create and testhypotheses relating to hybridization information to particularpathological or physiological states. A variety of statistical analysesare provided to suggest and evaluate hypotheses.

The instant invention also encompasses a method, wherein a Web user orbrowse user (e.g., Internet user), transmits existing processed chipdata, such as a hybridization profile, to the central data processingfacility along with the identity of the profile. This may be done bydirectly supplying the data via a secure network connection, or bysubmitting the data via a CD, or the like. The hybridization profile isthen compared with stored hybridization parameters at the central dataprocessing facility, and artificial intelligence routines determine themost likely pathological or physiological conditions suggested by theprofile supplied by the user. Accordingly, suggested methods oftreatment for the conditions, are returned to the user.

In yet another method of the instant invention, a Web user or browseuser may search the artificial intelligence system and view statisticalsummaries of the database. In this manner, a user would use the databaseto search, correlate, manipulate, and display existing data.

c) THE SYSTEM ARCHITECTURE

A key feature of the artificial intelligence system is the archiving ofall test data. All gene expression data that enters the system is both,used in diagnosis processing and archived for processing at a latertime. Significant upgrades in the diagnosis processing database occurover time which changes the clinical meaning of any given set of geneexpression data as new information is supplied to the system. The systemrepeatedly updates the existing information. For example, the clinicalinformation based on a gene expression data set may be different fromone year to another as the information is continuously compared to newfindings as a result of data influx and developments and advances inresearch and medicine. The system has the capability to identify whicharchived data should be processed based on the diagnosis processingdatabase history. In addition, the system has the ability to implementthe reprocessing. Email notification of revised results may be sent tomaster users and users, whenever data has been updated or reprocessed.

Another key feature of the system is that it provides immediate accessto all generated test results. Master users and users can view theentire history of all test results for a particular, related user. Thisis Well suited to be a key feature for clinical users and associatedclinicians and/or genetic counselors. This is also the mechanism throughwhich new test results derived from reprocessing of archived geneexpression data are available to any given user.

The system has two key databases for analysis of DNA chips. The firstdata base contains the probes (e.g., oligos such as 25mer, 50mer, 70mer,or cDNA fragments, etc.) representing specific genes and the genes'sequences (e.g., full length cDNA). Thus, the first database encompassesthe sequence tags for each spot on the chip. The gene targets for thesequence tags are defined by the genomics category and thebioinformatics category which process the specific sequence tags to beused in both chip production and clinical analysis of the test data. Thesecond database is the diagnosis processing database which contains thehybridization profiles and provides the diagnosis of the test results.This database relies on artificial intelligence to analyze and interpretthe gene expression data and other biological information (e.g., genomicdeletions, additions, transcription, etc.). Hence, this databasecontains a set of rules for the various combinations of gene expressionlevels and the associated diagnosed conditions with associated treatmentoptions. There is also an additional and optional diagnosis databasethat is used specifically for research and development related DNAchips.

The following section lists each element of the system, wherein thefunctionality and processing is defined in hierarchical form. Thisillustration provides an overview of the system architecture, includingthe server nodes and their associated functionality. An overview of theartificial intelligence system architecture is also shown in FIG. 1.

Diagnostic User Architecture

1.0 User commands

-   -   A. Application ID Select    -   1.2 Initiate scan & processing    -   1.3 Output data        -   1.3.1 Initiate transfer to central system for processing        -   1.3.2 Report generation

2.0 Remote processing

-   -   2.1 Data management        -   2.1.1 Memory search management        -   2.1.2 Data transfer from scanner/detector system memory    -   2.2 Genetic pattern generation        -   2.2.1 Single site pattern generation        -   2.2.2 Aggregation to multi site result    -   2.3 Generate data format for export to central system

Central Data Processing Facility Architecture

1.0 Web Server

-   -   1.1 Security        -   1.1.1 Browse user        -   1.1.2 Product ordering user        -   1.1.3 Database user    -   1.2 About Iris Biotech        -   1.2.1 Company        -   1.2.2 Products & services        -   1.2.3 Statistical summary of database (percent of matches,            etc.)        -   A. Product order management            -   1. Connect to operations server    -   1.4 Diagnostic/database user        -   1.4.1 Request to application server        -   1.4.2 Data transfer to application server

2.0 Operations Server

-   -   2.1 Product ordering management    -   2.2 Billing management    -   2.3 Order tracking management

3.0 Application Server

-   -   3.1 Accept genetic pattern and application chip ID data from Web        server    -   3.2 Database query for application chip specific data    -   3.3 Genetic pattern matching statistical processing    -   3.4 Report generation    -   3.5 Data transfer to database for particular user    -   3.6 Request to database server for browse of statistical summary        data

4.0 Database server

-   -   4.1 Genome data for each application chip    -   4.2 User specific data resulting from each use of an application        chip    -   4.3 Statistical summary of all application chip uses

The system architecture is based upon suitable server(s) and/or workstation(s) (e.g., servers and workstations that run on chips from IntelCorporation, IBM Corporation, or other manufacturers). Any suitablesoftware may be used with this system (e.g., Tuxedo® software from BEASystems Inc., and other applications). In addition, data bases may runon any suitable software that is compatible with the databases (e.g.,software from Oracle Corporation, or other software).

d) SYSTEM DESIGN AND SCALING

An overview of the system design and scaling is provided in FIGS. 2 and3. The system design focuses on distributed functionality for the keyfunctions of the system, and the associated ease of scalability when thesystem performance requirements increase with increasing chip sales.More specifically, the system design is based on a layered or tieredapproach. This allows for system scaling with minimum impact as thesystem performance requirements grow. The system includes a tier for theWeb server function, such as a Web Server Tier (see FIGS. 2 and 3). TheWeb Server Tier receives gene expression data, performs secure accessfunction(s), allows user registration, receives and forwards testresults queries, and receives and forwards transactions of geneexpression data for archiving and processing. The Web Server Tierutilizes a series of low end servers (e.g., servers from IntelCorporation) to perform the user interface and data transfer functions.For the purpose of illustration, four low end servers which provide thiscapability are depicted in FIG. 2.

The application tier in the system performs the diagnosis processingwhich converts gene expression data into test results (see FIGS. 2 and3). This tier utilizes a mid range server (e.g., E3500 Sun Enterpriseserver from Sun Microsystems, Inc.). This tier also performs thearchiving of the gene expression data using at least one of a family oftape drive library units (e.g., Sun tape drive library unit from SunMicrosystems, Inc.). The database tier (see FIGS. 2 and 3) whichperforms the storage and retrieval of the test results also utilizes amid range server (e.g., E3500 Sun enterprise server from SunMicrosystems, Inc.). This tier also utilizes a storage unit withredundancy for fault tolerance in which the test results are stored forrapid access (e.g., A5200 from Sun Microsystems, Inc.). FIG. 2illustrates the system design in logical form.

The system design is ideal for scalability (see FIG. 3). Hence, it meetsever increasing performance requirements. The Web server tier can be“scaled horizontally” by adding additional units in parallel. Theapplication tier can also be scaled horizontally by adding additionalunits in parallel. The database tier can be scaled both, horizontallywith additional units in parallel (or an increased number of processorsin the server), and vertically with additional storage units. Thisscaling concept is illustrated in FIG. 3. The scaling of any tier isindependent of the scaling of any other tier. This allows maximumflexibility. For example, it is possible to scale only one tier as thesystem demands change with respect to combination(s) of gene expressiondata sets, processing throughput, test data results storage, testresults queries, or the like.

e) RULES BASED SYSTEM

The automated artificial intelligence system of the instant inventionencompasses an expert system which identifies changes to underlyingassumptions for a Rules Base. The following description servers toillustrate the rules based system. It is understood, however, that theinvention is not limited to the specific examples disclosed in thissection.

In a rules based system, the set of rules (R) may change over period(s)of time (t).R(t ₀)=R _(t0)R(t _(n))=R _(tn)

Where t₀ is the creation of the rule set, and t_(n) is the n^(th)creation of the rule set.

The art describes using a mathematical or logic system as a simple setof rules to specify how to change one string of symbols into a set ofsymbols (J. Giarratano and G. Riley, Expert Systems: Principles andProgramming, 2nd Edition, PWS Publishing Company, 1994, p. 30). Hence,this leads to a simple translation of data and symbol sets.

Here, x,y with Luminescence (L) Value represents a disease (D)indicator.R₁ L(x,y) λ n→D

This can be translated into a conditional logic. According to Rule 1(R₁), if the luminescence at x, y is λ n, then Disease (D) exists in thesample. This does not limit the order of execution(s) of rules via acontrol strategy.

A Markov algorithm is an ordered group of productions which are appliedin order of priority to an input string (J. Giarratano and G. Riley,supra, p. 33). This allows for certain pre-tests to exist beforeperforming the analysis to conclude that D exists.R₂ L (a,b) λ BTV→BT (A0B)

Rule 2 (R₂) states if L at a,b is greater or equal to Blood Type Value(BTV), then Blood Type (BT) is A, 0, or B.

-   -   BT=Blood Type; BTV=Blood Type Value    -   Blood Type A=A    -   Blood Type B=B    -   Blood Type 0=0

The Markov algorithm allows the prioritization of the rules to beordered. In this case, the requirement of R₂ to hold prioritization P₁vs. R₁ to hold P₂ was not a known condition when R₁ was created. So,P₁ R₂ L(a,b) λ BTV→BT (A0B)R₂ R₁ L(x,y) λ n→D

The rate algorithm allows for fast pattern matching in large rule set(s)by storing information about the rules in a network. Instead of havingto match facts against every rule on every recognize-act cycle, the ratealgorithm only looks for changes in matches in every cycle (J.Giarratano and G. Riley, supra, p. 34). This means that the ratealgorithm looks at the change or delta (A) in patterns. Combined withMarkov algorithms, this leads to:

-   -   P₁ R₂        (ΔP_(1,2))(ΔR_(2,1))    -   R₂ R₁

which detects a change in the point indicator to initiate the rule ifthe values exist in the form of requisite values:

-   -   if (L (a,b) λ BTV) and (L(x,y) λ n)→then D

This illustrates one possible example of how to detect a change in thepoint indicator in order to predict any given disease. However, thesystem is flexible and adapts to new rules, thus:R₁≠R₂≠R_(n)

And the priority/prioritization (P) for execution may change in eachrule set, representing a transform (TR) in the prioritization of thedata represented in rules.

Thus, Transform (TR) Priority (P) may exist at a given time (t), sothat:TR(P(t))andTR(P(1))≠TR(P(2))≠TR(P(3))

Generally, the rate algorithm tracks only the changes, but does notgroup classes of changes or identify root causes behind changes. Thus,new rules may be applied to group classes of changes or identify rootcauses behind changes.(ΔP_(1,2))(ΔR_(2,1))→D

It is also relevant to identify and track additional information andcall these pieces of additional information assumption (A), for example,(A_(t)):

-   -   A₁        L(a,b)        BTV of (A,B)    -   A₂        L(a,b)        BTV of (A, 0, B)

In a dynamic application it may be necessary to determine what theunderlying assumptions to the valid data and/or result sets are. Thismay influence the potential entry point(s) for analysis and may indicatea need to preprocess or reprocess data against multiple rules fordescribing test results. Results 1: P₁ R₂ A₁

R₂ R₁ A₁

D Results 2: P₁ R₂ A₂

R₂ R₂ A₂

not D

Providing Results 2 (with A₂) in order to describe the underlying rootcause for results conveys more information to the user. Providing bothsets of results as well as describing the differing results to the usermay assist in developing trend data in a population set. For example,examining a population segment may provide information about severalindividuals reporting symptoms of low energy and out-of-breathness attime t(₁), t(₂), t(₃), and t(₄). A medical break-through occurs betweent(₄) and t(₅), identifying the symptoms of disease D₅. T₁ P₁ R₁ A₁

0 D₅ T₂ P₂ R₂ A₁

0 D₅ T₃ P₃ R₃ A₁

0 D₅ T₄ P₄ R₄ A₁

0 D₅ T₅ P₅ R₅ A₅

D₅

In the illustration above, the rate algorithm accounts for a change inthe rules, but does not identify a change or delta (Δ) in the underlyingassumptions.

The above rules based decision making process is only an example andservers to illustrate one embodiment of the instant invention. In analternative embodiment of the instant invention, neural networks may beused to accomplish the above illustrated objectives. In yet anotheralternative embodiment of the instant invention, other application(s)may be employed.

f) IMAGE PROCESSING SOFTWARE

The image processing software hosted in the PC's attached to the geneticprofiling microarrays systems deployed at user sites may or may not bean element of the artificial intelligence system. The image processingsoftware provides image data from test data for use in the artificialintelligence system. The key function of the image processing softwareis to generate an intensity level for each spot on the chip for eachfluorescent color used. The intensity data is normalized to bothpositive and negative control spots on the chip, thus it defines geneexpression levels. The data generated by the image processing softwareis then sent to the central processing facility of the artificialintelligence system for analysis. Comparative hybridization may also beused. There are several existing image processing applications that canbe used for microarray test data image processing. Examples are thesoftware associated with the GenePix™ 4000 scanner from AxonInstruments, Inc., the software associated with the ScanArray® 5000scanner from GSI Lumonics, and others. Stand alone microarray test dataimage processing software tools are optionally employed. Examplesinclude the IPLab in the Microarray Suite from Scanalytics Inc., andImaGene™ from BioDiscovery, Inc. The selection of image processingsoftware is usually selected based on the scanner system used.Alternatively, any image processing software that is compatible with thescanner system may be employed. Optionally, a custom scanner or ChargedCoupled Device (CCD) based system is available in conjunction with theartificial intelligence system.

g) EXAMPLES

The following specific examples are intended to illustrate the inventionand should not be construed as limiting the scope of the claims. Theexamples further illustrate some of the specifics within the artificialintelligence system and factors that effect how the system is used.

I. Integration of Public Databases (DBs) into the Database (DB) of theArtificial Intelligence System.

The following databases, databanks, information sources, and data areintegrated into the system of the instant invention, wherein informationis stored, downloaded, and upgraded routinely.

National Center for Biotechnology Information (NCBI)

GenBank

UniGene

GeneMap

EST, STS, and SNP Database(s)

Online Mendelian Inheritance in Man Database (OMIM™)

Diseases and Mutations

Blast Engine(s)

Others

National Library of Medicine (NLM)

Centers for Disease Control and Prevention (CDC)

Federal Drug Administration (FDA)

National Institute(s) of Health (NIH)

others

II. Data Mining from Public Information

The artificial intelligence system allows for data mining which includesmining for information such as:

Current research and development on genetic and medical sciences

New technologies (array technologies, diagnostic tools, drugdevelopment, genetics testing, high throughput screening, etc.)

Market information (domestic and international, basic research andclinical applications)

Competitor information

Political, economic, social (life style, healthcare, etc.) trends andchanges

III. Information for Major Decisions

The artificial intelligence system provides information related to thefollowing:

General Information

This information is related to management strategic decision(s), companydirection(s), finance(s), market targeting, and others.

Specific Information

This information is related to project decision(s), technologyapplication(s), research & development, product design, gene selection,and others. Information is correlated and integrated into the artificialintelligence system. The information is:

Market based

Disease based

Technology based

Species based

Function based

Pathway based

Sequence based

Mutation based

Cluster based

Disease-, Gene-, and Sequence Analysis Information

This analysis information is organized and stored in various databanks:

Disease gene(s) classification: Disease Databank

Pathway, interaction(s), and regulation(s) network: Pathway Databank

Clusters and their unique regions: Cluster Databank

Sequencing and oligonucleotide design: Oligo Databank

Mutations: Mutation Databank

IV. Organization of Genetic Materials

Genetic Materials are organized in a Gene Databank, wherein thisdatabank includes, but is not limited to:

Gene selection

Materials preparation or synthesis

Materials coding

Materials storage

Materials tracking

V Gene Selection

Specific computer program(s) are used in order to select genes ofinterest within the database(s). Thus, in selecting genes or segment(s)of genes to represent a particular gene, various computer programs areemployed. For example, a computer program may be used to select genessuch as BRCA1, BRCA2, HER2/neu, p53, and p57 as genes of interest to beput/added onto a Breast Cancer Gene Chip. For the BRCA1 gene, anothercomputer program may be used to select one or more unique 50mer oligosequence(s) with the desired GC content, minimal hairpin formation,minimal di-mer formation, and optimal melting temperature.

VI. Preparation of Genetic Materials

The preparation of genetic materials includes, but is not limited to:

High throughput amplification and purification

Oligo/peptide nucleic acid (PNA) design

Oligo/peptide nucleic acid (PNA) synthesis

Sequencing confirmation

Concentration adjustment

VII. Microarray Design and Tracking

Microarrays are designed and tracked via an Array Databank, wherein thisdatabank includes, but is not limited to:

Array design (e.g., artificial intelligence (AI); controls; programmedimage(s); grouped by disease(s), function(s), pathway(s); underlyingnetwork; phenotype-genotype correlation; and others)

Array location ID in conjunction with genetic materials information,sample ID, storage plates, and other parameters

Array ID connected to final imaging, data analysis and data export toend users

VIII. Spotting

Spotting encompasses inkjet printing system calibration(s) andmonitoring of key parameters.

IX. Tissue

Tissue that is being tested is tracked and recorded in a SampleDatabank. All tested tissues are recorded with respect to the followingparameters:

Sources: people/animal/other

Tissue type: e.g., blood, breast tissue, liver tissue, etc.; normaltissue, diseased tissue, compromised tissue, tumor tissue, stressedtissue, etc.

Diagnosis before testing

Treatment or test before genetic profiling microarray testing

Control sample information

Tissue preparation information/labeling procedure

other

X. Hybridization

Hybridization information includes, but is not limited to:

Programmed hybridization procedure(s) in conjunction with fluidicstation(s)

Hybridization condition(s) (e.g., buffer component, time cycle,temperature control, etc.)

Washing

Chip storage

XI. Results Analysis

The results analysis is divided into three stages, such as:

Image Analysis: Image Databank

Translate real image to analytical image

Transfer image to digital/number (pixel intensity)

Sorting, regrouping, comparing, filtering and highlighting significantchanges

Correlating to public and internal data

End user communication

Profiling: Profile Databank

Expression profile by different tissues, diseases, ethnic groups,treatments, pathway, genes, etc.

Mutation and Disease: Mutation Databank

Mutation DB: disease types, phenotype-genotype correlations

XII. Information Presented through a User Interface

The artificial intelligence system provides information to the user,through a network (e.g., Internet) via a user interface. Information ispresented through windows, screens, menus and the like, which allow theuser to conveniently view user information, clinical sample information,testing information, clinical test results report, R&D sampleinformation, chip information, results report for biopharma chip,therapeutic choices, billing information, and others.

The following are examples of information presented to the user via theuser interface:

User Information

User ID (user specific/secured)

Password (user specific/secured)

Name

Sex

Date of Birth

Ethnicity

Social Security Number (SSN)

Health History

Occupation

Employer Information

Insurance Information

Physician's Information/Clinic & Hospital Information

Family History

Diagnosis

-   -   a) Clinical/Physician    -   b) Pathology    -   c) Clinical/Lab    -   d) Genetic Test

Clinical Sample Information

Date

Sample ID

Patient ID

Organ

Tissue

Cell

DNA or mRNA

Preparation/Amplification/Purification

Labeling

Storage

Control Sample Information

Testing Information

Date of the Test

Type of Testing

Genetic Testing

Expression Profile

Classified Testing

Cancer

Cardiovascular

Neurological

Endocrinological

Infectious

Metabolic

Hematological

Immunological

Aging

Chip Used

Hybridization Method(s) & Condition(s)

Clinical Test Result Report

Date

Chip ID

Patient ID

Sample ID

Genetic Testing

Mutation

Amplification

Expression Profile

Abnormal Expression Pattern

Related Genes

R&D Sample Information

Source of the Samples

Human

Animal

Insect

Viral

Bacterial

Yeast

Agricultural

Others

Tissue(s) and Cell Type

Treatment

Reagent(s)

Concentration

Time Period

Specific Treatment

Sample Preparation Information

Labeling, Storage, and Hybridization Information

Control

Chip Information

Chip ID

Type of Chip

BioPharma

Custom Made (R&D)

Clinical

Chip Classification

Disease Specified

Function Specified

Mutation Related

Complete Probe Information

Complete Array Information

Direct Link of the Information to Genetic Database

Result Report for BioPharmaChip

Type of Experiment(s)

Expression

Mutation

Amplification

Chip Classification

Disease Based

Functionality Based

Structure Based

Top Hit List (e.g., Top 10 Hits, Top 100 Hits, etc.)

Over Expressed and Under Expressed Genes Compared with Controls

Mutations Generated or Detected

Genomic Amplification

Conclusions by Researcher(s), Physician(s), Genetic Counselor(s), etc.

Therapeutic Choices

Patient ID

Sample ID

Chip ID

Test Result and Diagnosis: Disease vs. Genotype/Expression Alterations

Available Therapeutics

Alternative Therapeutic Choices

Therapeutics under Development

Billing Information

Patient ID or Customer ID

Sample ID

Chip ID

Test Result

Insurance Billing Information

Doctor Fee

-   -   a) Reimbursement by Insurance Company

Patient Payment

Customer Payment

XII. Online Marketing, Ordering and Shipping System (B2B2C)

The artificial intelligence system includes an operations server whichstores information regarding orders, billing, order tracking, shipping,and others. E-commerce related information is also provided. E-commercetransactions may include patient(s) purchasing prescription drug(s);insurance companies offering discount(s) to individual(s) with healthyGenetic Profiles (GPs); farmer(s) purchasing new Genetically ModifiedOrganisms (GMOs); user(s) subscribing to specific news bulletin(s); anduser(s) ordering specific book(s) or other information material to helpthem understand specific genetic profile(s). The system also optionallyincludes educational information/seminar(s), and specific chat room(s)and gathering(s) of support groups on-line that may attract largenumber(s) of regular visitors, offering further advertisement optionsand facilitation of commerce involving a wide variety of products andservices.

XIV. End User Application

The end user application includes the following:

Windows based platform

Firewall protected entry

User password (PW) and sample ID specified log-on

Selected and limited access by diagnostic user and browse user

Online technical support system(s)

Various modifications and variations of the present invention will beapparent to those skilled in the art without departing from the scopeand spirit of the invention. Although the invention has been describedin connection with specific preferred embodiments, it should beunderstood that the invention as claimed should not be unduly limited tosuch specific embodiments. Indeed, various modifications of thedescribed modes for carrying out the invention which are obvious tothose skilled in the art are intended to be within the scope of theclaims.

1. An artificial intelligence system for the analysis of nucleic acidmicroarray hybridization information related to an individual patientfor diagnosing a physiological condition of said patient and forrecommending treatment for said patient, comprising: (i) a user facilitycomprising a nucleic acid microarray having a surface comprising nucleicacid probes comprising about 25 to about 70 bases in length tethered tosaid microarray surface, and an optical scanning system configured tocollect hybridization information from said microarray, saidhybridization information comprising gene expression information relatedto said patient, said user facility being configured to provide geneexpression information, (ii) a Web server that communicates with atleast one user facility, configured to perform a group of functionscomprising receiving and transmitting hybridization information relatedto said patient, supporting data analyses, and providing security andbusiness functions, wherein said gene expression information related tosaid patient comprises hybridization information collected from saidmicroarray contacted with a clinical sample related to said patient,(iii) a database server that is configured to perform a group offunctions comprising storing hybridization profiles, clinicalinformation associated with hybridization profiles, personal medicalhistory information related to the patient, treatments suitable fordiagnosed conditions related to hybridization profiles, data relatedinformation, and statistical information associated with hybridizationprofiles; and (iv) an application server that is configured to recognizea diagnostic master user corresponding to a user facility and adiagnostic user corresponding to an individual patient associated withsaid diagnostic master user, to facilitate information exchange betweenthe Web server and the database server, to analyze said hybridizationinformation through the use of a rate algorithm comprising detectinghigh gene expression levels, detecting low gene expression levels, andrelating said gene expression levels with patient family history todetermine a likelihood of developing a specific disease, and to performa group of functions comprising: diagnosing a physiological condition ofthe patient suggested by the rate algorithm, and recommending methods oftreatment for said patient based on the diagnosed physiologicalcondition.
 2. The system of claim 1, wherein the group of functionsperformed by said Web server further comprises functions selected fromthe group consisting of product information, product ordering, companyinformation, statistical summary of patient database, request to theapplication server, and security.
 3. The system of claim 1, wherein thedata stored by the database server further comprises data selected fromthe group consisting of genetic pattern database data for chipidentification, patient genetic pattern database data, and statisticaldata summary data.
 4. The system of claim 1, wherein the applicationserver constructs at least one query for the database server, andperforms at least one statistical comparison between hybridizationparameters received by the Web server and hybridization parameterssupplied by the database server.
 5. The system of claim 4, wherein theapplication server is further configured to perform functions selectedfrom the group of functions consisting of database query for chipidentification genetic pattern, database query for statistical datasummary, pattern match statistical processing, and results output. 6.The system of claim 1, wherein said artificial intelligence systemfurther comprises an operations server.
 7. The system of claim 6,wherein the operations server comprises functions selected from thegroup consisting of order management, billing management, and ordertracking.
 8. The system of claim 1, wherein the user facility is linkedto said artificial intelligence system through encrypted networkconnections.
 9. The system of claim 8, wherein the user facility is aremote user facility.
 10. The system of claim 8, wherein the userfacility is a local user facility.
 11. The system of claim 8, whereinthe user facility is selected from the group consisting of a hospital, aclinic, a research facility, a business, and a non-profit organization.12. The system of claim 8, wherein the user facility comprises: (i) animage processing system to convert optical data from the opticalscanning system into a set of hybridization parameters, (ii) a computerlinked to a network; and (iii) a user interface to display data relatedinformation.
 13. The system of claim 12, wherein the network is theInternet.
 14. The system of claim 12, wherein the user interface furthercomprises functions selected from the group of functions consisting ofmanipulating data, searching data, analyzing data, and displaying data.15. The system of claim 14, wherein the user interface further comprisesdisplayed information selected from the group consisting of userinformation, clinical sample information, testing information, clinicaltest results report, research and development sample information, chipinformation, results report for biopharma chip, therapeutic choices, andbilling information.
 16. The system of claim 12, wherein the datarelated information is selected from the group consisting ofhybridization information, patient information, statistical information,clinical information, medical information, diagnosis information,treatment information, biological information, product information, andcompany information.
 17. The system of claim 12, wherein the userfacility further comprises functions selected from the group consistingof genetic pattern processing, request for pattern match for chipidentifier, and report generation.
 18. The system of claim 8, whereinthe user facility comprises: (i) a computer linked to a network; and(ii) a user interface to display data related information.
 19. Thesystem of claim 18, wherein the data related information is selectedfrom the group consisting of hybridization information, patientinformation, statistical information, clinical information, medicalinformation, diagnosis information, treatment information, biologicalinformation, product information, and company information.
 20. Thesystem of claim 1, comprising a system architecture based on a sharedprocessing functionality between at least one remote location and atleast one central data processing facility.
 21. A method for diagnosinga physiological condition of an individual patient and for recommendingtreatment for said patient, comprising: (i) providing a patientidentifier related to said patient, (ii) providing a nucleic acidmicroarray having a surface comprising nucleic acid probes comprisingabout 25 to about 70 bases in length tethered to said microarraysurface; (iii) collecting hybridization information from said nucleicacid microarray related to said patient and said patient identifier,wherein said hybridization information related to said patient comprisesgene expression hybridization information collected from said nucleicacid microarray comprising nucleic acid probes comprising about 25 toabout 70 bases in length tethered to said microarray surface contactedwith a clinical sample related to said patient, (iv) transmitting saidhybridization information and said patient identifier information to acentral data processing facility, (v) analyzing said hybridizationinformation to generate a hybridization profile related to said patient,(vi) comparing said hybridization profile to stored hybridizationparameters, and to stored patient medical history information andclinical observations related to said patient to provide analyzed data,and (vii) diagnosing a probable physiological condition suggested bysaid analyzed data through the use of a rate algorithm comprisingdetecting high gene expression levels, detecting low gene expressionlevels, and relating said gene expression levels with patient familyhistory to determine a likelihood of developing a specific disease fordiagnosing a physiological condition of said patient, (viii)recommending methods of treatment for said patient based on thediagnosed physiological condition, patient personal medical history andclinical observations, and (ix) updating said stored hybridizationparameters and said stored patient medical history. 22-24. (canceled)25. A method for diagnosing a physiological condition of a livingorganism and for recommending treatment for said living organism,comprising: (i) providing an identifier related to said living organism,(ii) providing a nucleic acid microarray related to said living organismand said identifier, comprising nucleic acid probes comprising about 25to about 70 bases in length tethered to a microarray surface; (iii)collecting hybridization information from said nucleic acid microarrayrelated to said living organism and said identifier, (iv) transmittingsaid hybridization information and said identifier information to acentral data processing facility, (v) analyzing said hybridizationinformation to generate a hybridization profile related to said livingorganism, (vi) comparing said hybridization profile to storedhybridization parameters, disease models and living organism profiles toprovide analyzed data, (vii) determining a probable physiologicalcondition suggested by said analyzed data through the use of a ratealgorithm adapted to detect changes between said compared parameters andsaid profile comprising detecting high gene expression levels, detectinglow gene expression levels, and relating said gene expression levelswith stored hybridization parameters, disease models, and livingorganism profiles to determine a likelihood of developing a specificdisease for diagnosing a physiological condition of said livingorganism, (viii) recommending methods of treatment for said livingorganism based on the diagnosed physiological condition, disease models,and living organism profiles, and (ix) updating said storedhybridization parameters and living organism profiles.
 26. The methodfor diagnosing a physiological condition of an organism and forrecommending treatment for said organism of claim 25, wherein the livingorganism is an animal.
 27. The method for diagnosing a physiologicalcondition of an organism and for recommending treatment for saidorganism of claim 25, wherein the living organism is a plant.